Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes–Mallows index, are often used to evaluate performance of machine learning models, in particular, when facing imbalanced datasets that contain more samples one class than other. Recent optimal decision tree algorithms have shown remarkable progress producing trees with respect linear criteria, accuracy, but un...

In this paper, we study arbitrary regular factorial languages over a finite alphabet ?. For the set of words L(n) length n belonging to language L, investigate depth decision trees solving recognition and membership problems deterministically nondeterministically. case problem, for given word from L(n), should recognize it using queries each which, some i?{1,…,n}, returns ith letter word. ? n, ...

Modern speech recognition systems typically cluster triphone phonetic contexts using decision trees. In this paper we describe a way to build multiple complementary decision trees from the same data, for the purpose of system combination. We do this by jointly building the decision trees using an objective function that has an added entropy term to encourage diversity among the decision trees. ...

This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from...